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Software Test Data Generation Based on Improved Particle Swarm Optimization Algorithm

Dan Liu, Jianmin Wang


Software testing is an important means of software quality assurance, the automatic generation of test data has been widely studied. By analyzing the advantages and disadvantages of the genetic algorithm ?the particle swarm optimization algorithm and the ant colony algorithm, the paper proposes a new improved particle swarm optimization algorithm in the automatic generation of test data. By the artificial immune algorithm is introduced into the particle swarm algorithm, the diversity of the individual is kept in the improved strategy, and it can overcome the local optimum problem of standard particle swarm optimization algorithm. The overall search capability as well as the performance of the standard algorithm is enhanced. Finally experiment proves the feasibility and efficiency of the algorithm in software testing.


Test data, particle swarm optimization algorithm, immune algorithm.

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